The joint network was fine-tuned for an additional 100k steps to minimize \(\mathcal {L}^{\mbox{\tiny {ASR}}} + \eta \mathcal {L}^{\mbox{\tiny {SNRi}}}\)  where the tunable hyperparameter \(\eta = 0.01\)  was determined based on WER on the validation dataset. In the fine-tune stage, to reduce artifacts in the output signal, the squared-error between target and predicted SNRi was weighted by a factor of 0.1, and the regularization factor was increased as \(\beta = 1.0\) . Also, we skipped the SE frontend with a probability of 5% as a multi-condition learning strategy [1]}. In addition, we used a random \(\lambda \)  drawn from \(\mathcal {U}(\lambda _{\min },\lambda _{\max })\)  instead of the predicted one with 25% probability. All training used the Adam optimizer [2]} with the same setting as [3]} except for using 1/10 learning rate in the fine-tuning stage, and 128 Google TPUv3 cores with a global batch size of 512.
